code: def test_AUTO_stopping_metric_with_custom_sorting_metric_regression():
      print(
          "Check leaderboard with AUTO stopping metric and rmse sorting metric"
      )
      ds = import_dataset("regression", split=False)
      exclude_algos = ["DeepLearning", "GLM"]
      aml = H2OAutoML(
          project_name="py_aml_lb_test_auto_stopping_metric_custom_sorting",
          exclude_algos=exclude_algos,
          max_models=10,
          nfolds=2,
          stopping_rounds=1,
          stopping_tolerance=0.5,
          seed=automl_seed,
          sort_metric="rmse",
      )
      aml.train(y=ds.target, training_frame=ds.train)
  
      check_leaderboard(
          aml,
          exclude_algos,
          ["rmse", "mse", "mae", "rmsle", "mean_residual_deviance"],
          "rmse",
      )
      base = get_partitioned_model_names(aml.leaderboard).base
      check_model_property(base, "stopping_metric", True, "deviance")
  
file_path: h2o-3/h2o-py/tests/testdir_algos/automl/leaderboard/pyunit_automl_leaderboard_regression.py
imports: import os
  import sys
  from h2o.automl import H2OAutoML
  from tests import pyunit_utils
  from _leaderboard_utils import check_leaderboard, check_model_property
  from _automl_utils import import_dataset, get_partitioned_model_names
label_desc: Use image node_id 5 for calling a global function with example usage: test_AUTO_stopping_metric_with_custom_sorting_metric_regression() without return types